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Carl Edward Rasmussen

Carl Edward Rasmussen
Professor of Machine Learning
Department of Engineering
  Cambridge University
Chief Scientist and Chairman
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ELLIS Society
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The Alan Turing Institute
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Darwin College
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I'm a professor in the Machine Learning Group and head of the Computational and Biological Learning Lab in the Division of Information Engineering at the Department of Engineering in Cambridge. I work on machine learning and on climate change. I don't travel professionally by air because it destroys the habitability of earth.


I'm interested in the theory and practice of understanding and building systems that learn and make decisions. Humans have an exceptional ability to learn from experience, which sets them apart from current artificial intelligent (AI) systems. To understand human learning and design better AI we need principled approaches to learning and decision making based on Bayesian inference in machine learning. My interests span: probabilistic inference, reinforcement learning, approximate inference (variational and MCMC), decision making, non-parametric modeling, stochastic processes and efficient learning.

My first mentor was David Willshaw; I completed my MSc with Lars Kai Hansen and PhD with Geoff Hinton.


Gaussian Processes

Gaussian processes (GPs) are a principled, practical, probabilistic approach to learning in flexible non-parametric models. GPs have found numerous applications in regression, classification, unsupervised learning and reinforcement learning. Great advances have been made recently in sparse approximations and approximate inference. My book Gaussian Processes for Machine Learning, MIT Press 2006, with Chris Williams is freely available online. I also maintain the gpml matlab/octave toolbox with Hannes Nickisch, as well as the pretty outdated Gaussian Process website.
 Gaussian Processes for Machine Learning cover

Random pointers

Are you fooled by sustainable growth?
Sustainable Energy - without the hot air, facts about sustainable energy by David MacKay.
What is Science?, by Richard Feynman, 1966.


Probabilistic Machine Learning, 4th year module 4F13, also part of the MPhil for Machine Learning and Machine Intelligence

Students and Postdocs

Talay Cheema
Miguel Garcia-Ortegon
Stratis Markou


Matthias Bauer, Research Scientist at DeepMind, London
David Burt, postdoc at MIT
Jan-Peter Calliess, Senior Research Fellow, Oxford-Man Institute of Quantitative Finance and Department of Engineering Science, Oxford
Lehel Csató, Professor of Computer Science, University of Babes-Bolyai, Romania
Marc Deisenroth, Professor of Artificial Intellgence, University College London
David Duvenaud, Assistant Professor in Computer Science and Statistics, Univeristy of Toronto
Roger Frigola, Data Science Consultant, Barcelona
Adrià Garriga-Alonso, researcher scientist, FAR AI
Dilan Görür, Research Scientist, DeepMind, San Francisco
Matthew W. Hoffman, Research Scientist, DeepMind
Ferenc Huszár, Senior Lecturer in Machine Learning, Department of Computer Science and Technology, Cambridge University
Alessandro Davide Ialongo, founder and CTO at
Manon Kok, Assistant Professor at Delft Center for Systems and Control, Delft University of Technology
Niki Kilbertus, group leader at Helmholtz AI in München
Malte Kuß, Director Controlling & Risk Management at RWE AG, Essen
Vidhi Lalchand, MIT and Harvard Broad Institute
Andrew McHutchon, Data Scientist, McLaren Racing Limited, Woking
Rowan McAllister, post doc, EECS, UC Berkeley
Hannes Nickisch, Senior Scientist, Philips Research, Hamburg
Sebastian W. Ober Senior Machine Learning Engineer at AstraZeneca
Tobias Pfingsten, Team Manager, Boston Consulting Group, Düsseldorf
Robert Pinsler, Senior Researcher at Microsaoft Research in Amsterdam
Joaquin Quiñonero Candela, Director of Applied Machine Learning, Facebook
Paul Rubenstein, Machine Learning Research Engineer, Apple, Zürich
Yunus Saatçi, Machine Learning Scientist, Uber AI Labs
Ushmish Sengupta, Senior AI Researcher, Mediatek Research
Ryan Turner, Machine Learing Researcher, Montreal Institute for Learning Algorithms
Mark van der Wilk, University Lecturer, Department of Computing, Imperial College London
Andrew Gordon Wilson, Associate Professor, New York University

Contact Information

Department of Engineering
Trumpington Street
Cambridge, CB2 1PZ, UK
voice +44 (0) 1223 748 513
fax +44 (0) 1223 332 662
email email address
PGP public key

My office is on the fourth floor of the Baker Building room number BE4-42.

© Cambridge University Engineering Dept
Information provided by Carl Edward Rasmussen (cer54)
Last updated: October 18th 2023